| --- |
| license: mit |
| datasets: |
| - ThinothW/Deepfake-Identity-Isolated-Dataset-PreP |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| - roc_auc |
| base_model: |
| - google/efficientnet-b1 |
| - mobilint/RegNet_Y_800MF.tv2_in1k |
| pipeline_tag: image-classification |
| tags: |
| - deepfake-detection |
| - image-classification |
| - efficientnet |
| - regnet |
| - hybrid-model |
| - computer-vision |
| --- |
| |
| # Deep_Fake_Hybrid_Model |
| |
| ### Detecting Deepfake Faces: An Image Classification Approach to Safeguarding Digital Identity |
| |
| A binary image classifier that flags a given face image as **real** or **fake** (deepfake / synthetically manipulated). Built as a **hybrid dual-backbone** model combining fine-tuned [`google/efficientnet-b1`](https://huggingface.co/google/efficientnet-b1) and [`mobilint/RegNet_Y_800MF.tv2_in1k`](https://huggingface.co/mobilint/RegNet_Y_800MF.tv2_in1k) feature extractors, trained on the [`ThinothW/Deepfake-Identity-Isolated-Dataset-PreP`](https://huggingface.co/datasets/ThinothW/Deepfake-Identity-Isolated-Dataset-PreP) dataset. |
| |
| This model was built as part of a university course project (AI Lab, SE334) exploring deepfake face detection. |
| |
| **Author:** S. M. Nihal Ahmed |
| |
| ## Model Details |
| |
| - **Base models:** `google/efficientnet-b1`, `mobilint/RegNet_Y_800MF.tv2_in1k` |
| - **Task:** Binary image classification (`fake` vs `real`) |
| - **License:** MIT |
| - **Architecture:** Hybrid dual-backbone — EfficientNet-B1 and RegNetY-800MF feature extractors, both fine-tuned end-to-end, with their pooled features fused and passed through a classification head |
| - **Fine-tuning objective:** Cross-entropy loss over the two classes, with `sklearn` balanced class weights applied to account for class imbalance |
| - **Training regime:** Mixed-precision (AMP) training on a CUDA GPU, up to 100 epochs per stage with early stopping (patience = 5, monitored on validation loss); two-stage schedule — Stage A trains only the fusion head with both backbones frozen, Stage B fine-tunes both backbones together at a lower learning rate |
|
|
| ## Intended Use |
|
|
| This model is intended for detecting AI-generated or manipulated (deepfake) face images versus authentic (real) face images. Example use cases: |
|
|
| - Screening uploaded profile/identity photos for synthetic manipulation |
| - Research and coursework on deepfake detection and media forensics |
| - A component in a larger content-authenticity verification pipeline |
|
|
| **Out of scope:** This model is **not** a complete or production-ready deepfake detection guardrail. It has been evaluated on one dataset only, and will not necessarily generalize to deepfake generation methods, image qualities, or demographics absent from its training data. |
|
|
| ## How to Use |
|
|
| This model is distributed as an **ONNX** export. Download both files and keep them in the same folder — the `.onnx` graph loads its weights from the `.onnx.data` file alongside it at runtime: |
|
|
| - [`deepfake_hybrid_final.onnx`](https://huggingface.co/nihal4/Deep_Fake_Hybrid_Model/resolve/main/deepfake_hybrid_final.onnx) — the ONNX graph |
| - [`deepfake_hybrid_final.onnx.data`](https://huggingface.co/nihal4/Deep_Fake_Hybrid_Model/resolve/main/deepfake_hybrid_final.onnx.data) — the external weights file |
|
|
| Install dependencies: |
|
|
| ```bash |
| pip install onnxruntime huggingface_hub pillow numpy |
| ``` |
|
|
| ### Single-image prediction |
|
|
| ```python |
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image |
| from huggingface_hub import hf_hub_download |
| |
| REPO_ID = "nihal4/Deep_Fake_Hybrid_Model" |
| IMG_SIZE = 260 |
| IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) |
| IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) |
| LABEL_MAP = {0: "fake", 1: "real"} |
| |
| # Downloads both files into the same local cache folder — required, since the |
| # .onnx graph references .onnx.data by relative path at load time. |
| onnx_path = hf_hub_download(repo_id=REPO_ID, filename="deepfake_hybrid_final.onnx") |
| hf_hub_download(repo_id=REPO_ID, filename="deepfake_hybrid_final.onnx.data") |
| |
| session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"]) |
| input_name = session.get_inputs()[0].name |
| output_name = session.get_outputs()[0].name |
| |
| def preprocess_pil(img: Image.Image) -> np.ndarray: |
| img = img.convert("RGB").resize((IMG_SIZE, IMG_SIZE)) |
| arr = np.asarray(img, dtype=np.float32) / 255.0 # HWC, [0,1] |
| arr = (arr - IMAGENET_MEAN) / IMAGENET_STD # normalize, same stats as training |
| return arr.transpose(2, 0, 1) # HWC -> CHW |
| |
| def softmax(x: np.ndarray) -> np.ndarray: |
| e = np.exp(x - x.max(axis=1, keepdims=True)) |
| return e / e.sum(axis=1, keepdims=True) |
| |
| def predict(image_path: str): |
| image = Image.open(image_path) |
| x = preprocess_pil(image)[np.newaxis, ...].astype(np.float32) |
| logits = session.run([output_name], {input_name: x})[0] |
| probs = softmax(logits)[0] |
| label = LABEL_MAP[int(probs.argmax())] |
| return label, probs |
| |
| label, probs = predict("path/to/face.jpg") |
| print(f"Prediction: {label} (p_fake={probs[0]:.3f}, p_real={probs[1]:.3f})") |
| ``` |
|
|
| ### Batch prediction |
|
|
| ```python |
| image_paths = ["face1.jpg", "face2.jpg", "face3.jpg"] |
| |
| batch = np.stack([preprocess_pil(Image.open(p)) for p in image_paths]).astype(np.float32) |
| logits = session.run([output_name], {input_name: batch})[0] |
| probs = softmax(logits) |
| preds = probs.argmax(axis=1) |
| |
| for path, pred, p in zip(image_paths, preds, probs): |
| print(f"{path}: {LABEL_MAP[int(pred)]} (p_fake={p[0]:.3f}, p_real={p[1]:.3f})") |
| ``` |
|
|
| > For GPU inference, install `onnxruntime-gpu` instead and pass `providers=["CUDAExecutionProvider", "CPUExecutionProvider"]` when creating the session. |
|
|
| ## Training Data |
|
|
| The model was fine-tuned on the [`ThinothW/Deepfake-Identity-Isolated-Dataset-PreP`](https://huggingface.co/datasets/ThinothW/Deepfake-Identity-Isolated-Dataset-PreP) dataset. |
|
|
| - **Labels:** `0 = fake`, `1 = real` |
| - **Splits:** train / validation / test |
| - **Preprocessing:** resize to 260×260, ImageNet normalization (mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]`) |
| - **Training augmentation:** random horizontal flip, random rotation (±10°), color jitter, plus simulated JPEG compression and simulated blur/downscale-upscale (to reduce false positives on low-quality real footage) |
| - **Class balancing:** `sklearn` balanced class weights applied in the loss function to address train-set class imbalance |
|
|
| ## Training Procedure |
|
|
| <!-- PLACEHOLDER: training curves (loss/accuracy per epoch) — image to be uploaded --> |
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|  |
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|
| - **Framework:** PyTorch |
| - **Hardware:** Kaggle free-tier T4 GPU |
| - **Loss:** Cross-entropy |
| - **Mixed precision:** Enabled (AMP) |
|
|
| ## Evaluation |
|
|
| Evaluated on the held-out test split (n = 21,316) at a decision threshold of 0.5. |
|
|
| ### Classification Report |
|
|
| | Class | Precision | Recall | F1-score | Support | |
| |--------------|:---------:|:------:|:--------:|:-------:| |
| | fake | 0.95 | 0.96 | 0.95 | 10,706 | |
| | real | 0.96 | 0.95 | 0.95 | 10,610 | |
| | **accuracy** | | | **0.9539** | 21,316 | |
| | macro avg | 0.95 | 0.95 | 0.95 | 21,316 | |
| | weighted avg | 0.95 | 0.95 | 0.95 | 21,316 | |
|
|
| **Test ROC-AUC:** 0.9903 |
|
|
| ### Confusion Matrix |
|
|
| <!-- PLACEHOLDER: image to be uploaded --> |
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| ### ROC Curve |
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| <!-- PLACEHOLDER: image to be uploaded --> |
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| ## Limitations |
|
|
| - Performance is reported on a single dataset; generalization to other deepfake generation methods, image resolutions, compression levels, or demographics is not guaranteed. |
| - As with most deepfake detectors, robustness against novel/unseen generative techniques (including adversarially crafted ones) has not been evaluated here. |
| - The model has not been evaluated as a standalone production guardrail; it is intended to complement, not replace, other verification measures. |
|
|
| ## Citation |
|
|
| If you use this model, please cite this repository and reference this course project: |
|
|
| ``` |
| @misc{deepfake-hybrid-detector, |
| title = {Detecting Deepfake Faces: An Image Classification Approach to Safeguarding Digital Identity}, |
| author = {S. M. Nihal Ahmed}, |
| year = {2026}, |
| note = {Course project, AI Lab (SE334), Daffodil International University} |
| } |
| ``` |
